On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters
Machine Learning
2021-12-14 v1 Neural and Evolutionary Computing
Abstract
Bloom Filters are a fundamental and pervasive data structure. Within the growing area of Learned Data Structures, several Learned versions of Bloom Filters have been considered, yielding advantages over classic Filters. Each of them uses a classifier, which is the Learned part of the data structure. Although it has a central role in those new filters, and its space footprint as well as classification time may affect the performance of the Learned Filter, no systematic study of which specific classifier to use in which circumstances is available. We report progress in this area here, providing also initial guidelines on which classifier to choose among five classic classification paradigms.
Cite
@article{arxiv.2112.06563,
title = {On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters},
author = {Giacomo Fumagalli and Davide Raimondi and Raffaele Giancarlo and Dario Malchiodi and Marco Frasca},
journal= {arXiv preprint arXiv:2112.06563},
year = {2021}
}
Comments
ICPRAM 2022